a cooperative position fix scheme based on a group management on

JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 26, 15-26 (2010)

15

A Cooperative Position Fix Scheme Based on a Group Management on the Vehicular Network*

JUNGHOON LEE, GYUNG-LEEN PARK, CHANG OAN SUNG+ AND HYUNG DO CHOI++

Department of Computer Science and Statistics Cheju National University Jeju-Do, 690-756 Korea

+Computer Science Department Indiana University Southeast New Albany, IN 47150, U.S.A.

++Radio Technology Department ETRI, Daejon, 305-700 Korea

This paper proposes and evaluates the performance of a cooperative position fix

scheme built on top of a group management mechanism carried out by processes distrib-uted across the vehicular network. The main goal is to enhance the accuracy of map matching and thus the quality of diverse location-based services without extra equipment. Designed on the telematics network consisting of many vehicles simultaneously moving on the road network, the proposed scheme enables the in-vehicle telematics device to play a role of collecting GPS readings from its neighbors through an appropriate wireless network interface when necessary. With at least 3 affirmative points having just a single segment to match, the GPS deviation for each time instant can be estimated by solving an equation system which has more variables than equations, and the estimated term im-proves the accuracy of positioning data. The performance measurement result shows that the proposed scheme can reduce the ratio of ambiguous points from 32.0% to 20.6%, while increasing that of affirmative points from 61.0% to 71.2%, sharpening the gap between the best two matches. Keywords: vehicular telematics, embedded operating system, group management, coop-erative computing, position fix

1. INTRODUCTION

As the blending of computers and wireless telecommunication technologies, the te-lematics system, especially vehicular telematics, is capable of efficiently conveying in-formation over vast networks to invite and improve a host of business functions or gov-ernment-related public services [1]. This system essentially involves a telematics device installed within a car in providing various LBSs (Location-Based Services) such as car navigation, vehicle tracking, and automatic collision notification [2]. Moreover, it be-comes possible for a car to carry a series of sensors capable of monitoring the internal status of a vehicle as well as the outer environmental data, for example, pollution detec-tor and even a camera, creating a stream-like sensor data. The operating system for this in-vehicle device is gradually drawing attention from diverse areas. For example, Mi-crosoft released Windows Automotive, another version of Windows CE embedded oper-ating system, for the purpose of providing an efficient running and developing environ- Received September 30, 2008; accepted January 7, 2009. Communicated by Sung Y. Shin, Jiman Hong and Tei-Wei Kuo. * This work was supported by the Korea Research Foundation Grant No. KRF-2008-013-D00096.

JUNGHOON LEE, GYUNG-LEEN PARK, CHANG OAN SUNG AND HYUNG DO CHOI

16

ment for an LBS application [3]. Meanwhile, as each vehicle can move just along the road network, accurate map

matching, which maps a current position onto a vector representation of a road network, is the most fundamental step for location-based services on the vehicular network, as most services can work only after knowing the exact road segment a vehicle is currently moving along. For a given position represented by latitude and longitude, sometimes along with altitude, the map matching procedure necessarily involves two components, namely, a positioning technique and a digital map. It is true that there are many position-ing technologies, GPS (Global Positioning System) is the most common choice for out-door area as it offers near-worldwide coverage. Accordingly, the GPS receiver is gener-ally integrated in most telematics devices [4]. However, map matching cannot be free from imprecision. First, the positioning error of GPS can be as large as tens of meters, induced by atmospheric effects, multipath effects, and clock errors. Second, the digital map also has not a little mismatch with the actual geographical shape due to plotting er-ror, map resolution, and piecewise linear links to approximate road curvature [5]. Errors in digitizing map can be quantified by the distance from the road segment to the both ends of a road, reaching sometimes nearly 20 meters [6].

Map matching of a specific location can exploit both previous and subsequent points in the post hoc analysis of GPS track, taking account of continuity of the driven path. However, in the real-time matching procedure, only the past points can be available and even in the case, the number of available points is limited to generate a timely re-sponse [7]. Moreover, when the positioning interval gets a little bit longer, the past data are also of no use. Accordingly, it is necessary to improve the accuracy of positioning mechanisms as much as possible. As an example, DGPS (Differential GPS) uses a fixed GPS receiver of predetermined location to measure the errors introduced by GPS. The measured error is sent to mobile platforms in the vicinity to be used to correct GPS posi-tions. However, DGPS is available not in every area or not in every GPS receiver, so its exploitation is very restrictive. Other methods combine secondary positioning mecha-nism such as local sensors, dead reckoning, and signal strength from the wireless base station, but they also need extra hardware or the interface implementation of vendor- specific components [7].

According to the steady dissemination of telematics devices having one or more network interfaces such as CDMA (Code Division Multiple Access), WLAN (Wireless Local Area Network), and VANET (Vehicular Ad-Hoc NETwork), it is possible to build a telematics network that spans a wide area. The network can be either infrastructure- based or ad-hoc style, depending on its air interface. In the first case, every telematics device is connected directly to a central server via the specific telecommunication carrier in the same way as the cellular phone. But this connection generally costs too much and suffers from limited bandwidth. As contrast, built upon the DSRC (Dedicated Short Range Communication) protocol, VANET enables each device directly to communicate with its neighbors residing in its transmission range [8]. It has to communicate with other telematics devices not residing in the range through multi-hop routing [9]. Even though network connectivity is not fully supported, this network eliminates connection fee and provides high data transmission rate. In either case, it is possible for a group of telematics devices to perform a common task cooperating via the network connection, just like dis-tributed processes.

A COOPERATIVE POSITION FIX SCHEME BASED ON A GROUP MANAGEMENT

17

In this paper, we are to address that the accuracy of GPS can be enhanced by a co-operative position fix scheme base on just the already-endorsed standard communication channel without additional hardware. Through the vehicular network, a cooperative group can be formed among telematics devices, making it possible for each of them to collect GPS records having the common time index from its neighbors. Now, the main idea of this paper begins with the property that despite the positioning error, some points are sure to be mapped to a unique road segment, especially when it is not in the high- density road area. Based on those points, the GPS pointing error of the given time in-stance can be estimated, as the telematics device in the vicinity experiences similar error characteristics [10]. The estimated term can not only correct the GPS error but also re-solve ambiguity in matching other points of a time stamp. The idea is somewhat inspired by DGPS, but it can be implemented without special equipment or vendor-specific communication channel, just based on the underlying operating system function and network connectivity supported in the telematics network. This improvement can directly lead to accurate map matching and thus better quality of location-based services.

This paper is organized as follows: After issuing the problem in section 1, section 2 provides the background of this paper and related work. Then, section 3 describes the cooperative position fix scheme in detail. After demonstrating the performance meas-urement results in section 4, section 5 summarizes and concludes this paper with a brief description on future work.

2. BACKGROUND AND RELATED WORK

2.1 Taxi Telematics System

Jeju Island is a popular vacation spot not just for Koreans but also many interna-tional visitors, having many tourist attractions as well as a well maintained road network which essentially follows the entire coast (200 km) and crisscrosses [11]. By industrial and academic projects embarked from Jeju telematics model city enterprise, telematics devices are popularized for both rent-a-cars and taxis. This device plays a role of an in-vehicle computing system to facilitate the customization of information service and contents to the travelers [12]. Particularly, the in-vehicle device in taxis contains a GPS receiver as well as an air interface, which follows CDMA protocol in Korea. Based on such components, each vehicle reports its location and speed every minute for a reason-able communication cost negotiated with a telecommunication company. The network interface has a plan to add a VANET interface to support instantaneous car accident noti-fication, collision avoidance, and other safety applications. After all, Jeju area possesses a telematics network consist of a lot of active telematics devices, making it possible to design, develop, and test diverse challenging services. 2.2 Accuracy Enhancement Schemes

GPS navigation requires three and preferably four or more GPS satellites to be in view at all times. However, screening by foliage and terrain are serious problems in many locations [2]. The error in a positional GPS measurement is known to follow the


18

Gaussian distribution, whose horizontal distribution is circular. As such, the GPS error can be substantial in certain condition (shadowed and reflected signals), typical error of GPS signal reaches tens of meters. Hence, some GPS readings do not lie exactly on the road network segment, resulting in ambiguity to find the segment a vehicle is currently moving. Hence, there have been many accuracy enhancement schemes ranging from a pure software solution to special hardware employment.

Kalman filter is one of the most prominent software-based schemes to enhance po-sitioning accuracy, capable of providing a well-established statistical estimation model capable of dealing with noisy data such as time-indexed GPS location tracks [13]. State information is maintained as a state transition matrix and internal parameters to catch up with the Gaussian error distribution. It has another advantage that it can consider addi-tional data like velocity to further improve accuracy. However, Kalman filter can not benefit from the cooperative computing because of the tight data dependency across it-erative refinement steps.

Some schemes exploit and combine secondary positioning methods to the stand-alone GPS data [7]. Those sensors include a built-in speedometer and a fiber optic gyroscope. However, they are rarely installed in the common telematics device, making it necessary to attach additional vendor-specific interfaces. In addition, such a method as dead reck-oning estimates the current locations of vehicles based on the movement they made since their last known location, but its main goal is just to complement GPS-blind area, not to enhance positioning accuracy.

As one of the most successful accuracy improvement techniques, DGPS makes fixed stations, which know their absolute coordinate in advance, constantly compare the real position with the position given by the GPS system. Due to a lot of influences there will be always a difference between these two positions, namely, between real and esti-mated positions [13]. This considered error is common for the position of the station but also for positions in a wide range up to hundreds of kilometers around the station [6]. So the GPS users within this range can use this error information for improved GPS per-formance. However, error information must be sent from the DGPS-station to users via some other communication channel, for example, FM subcarrier. To this end, the users need a special DGPS receiver. Even though DGPS achieves the positional accuracy of 5m, the use of real-time DGPS in moving vehicle requires additional data exchange of pseudorange corrections.

3. GROUP-BASED GPS

3.1 Group Management

Basically, each telematics device is equipped with a GPS receiver as well as a wire-less interface through which a group management protocol can be carried out. A legacy neighbor discovery protocol is available on the service of underlying networks [14], and most operating systems possibly support the group management at a network driver level, a system service, or even in the application process. This membership management is especially essential for the cache management in the mobile devices and can be made available to other services [15]. In this protocol, the neighbor connectivity is maintained


19

through a periodic beacon of “hello” message with corresponding information such as a sender identifier. As such, each telematics device can know the 1-hop neighbors, while n-hop neighbor searches can be performed when necessary. In addition, for more reliable communication, we can exploit even a GPS-based message broadcast [16]. Generally, the more information is available for a group, the better the result of a cooperation process will be. The number of members in a group depends on the transmission range of a wire-less interface as well as the vehicle density distribution in the target group. We found that the number of members is almost always enough to fulfill distributed applications from the connectivity analysis based on the real-life location history data obtained form the Taxi telematics system [17]. 3.2 Main Idea

Fig. 1 illustrates the main idea. P1 and P2 are the current GPS readings of two mov-ing vehicles at a specific time instant, but each of them is not exactly on any road seg-ment. If we run a map matching procedure based on the closest distance scheme, P2 has three candidate segments, namely, L3, L4, and L5. They are located at bottom, right, and top of P2, respectively. It is ambiguous to which segment P2 matches. On the other hand, P1 intuitively has a unique candidate, L2, at its right. This situation results from the fact that P2 is located in the relatively dense road area while P1 is not. The point such as P1 having a unique candidate is defined as an affirmative point, while others as ambiguous points. The affirmative point can give a useful hint to ambiguous points in selecting their matching segments among many candidates. If there were neither GPS error nor map imprecision, the real position of the vehicle would be somewhere on the segments. Con-sidering just GPS error, P1 is left to L2, so we can infer that P1 and P2 are skewed to the left, leading to the high possibility that P2 is on L4.

Fig. 1. Main concept.


20

Above assertion may help to correct the GPS error, but it has many issues to refine. In Fig. 1 again, the actual point of P1 may be on the segment at the top of it, namely, L1. In that case, P2 is matched to L6 not L4. This is possible because L1 is located upward from P1 at the same perpendicular distance, say d, as L6 is from P2. Now, we have an-other affirmative point, P3, and it tells that GPS deviation is to the left and also that P2 is matched to segment L4, as there is no segment at d upward away from P3.

Fig. 2. Basic concept.

A link is defined as a series of segments between two intersections. In downtown

area, many links consist of just a single segment, as they are mainly straight lines and their distances are relatively short. Contrarily, the curved link needs many segments to represent its geographical shape. In Fig. 2, two positions are marked as P1(x1, y1) and P2(x2, y2). We assume that there is no map digitization error while Δx and Δy are GPS deviations on latitude and longitude, respectively. (Δx, Δy) is common to both P1 and P2, as the two points are assumed to have experienced the same error. Assume that link f is the unique candidate for P1 and f consist of a series of segments, f1, f2, …, fl, each of which has its own linear equation F1, F2, …, Fl. Then, (x1 + Δx, y1 + Δy) should meet one of the following equations to be on link f.

F1(x1 + Δx, y1 + Δy) = 0 (1-1) F2(x1 + Δx, y1 + Δy) = 0 (1-2) … Fl(x1 + Δx, y1 + Δy) = 0 (1-l) However, with just one affirmative point, we can know nothing but that P1 is some-

where on a segment of f. Now, let P2 be another affirmative point and g consist of g1, g2, …, gm, each of which also has its own linear equation G1, G2, …, Gm. Then, (x2 + Δx, y2 + Δy) should meet one of the following equations:

G1(x2 + Δx, y2 + Δy) = 0 (2-1) G2(x2 + Δx, y2 + Δy) = 0 (2-2) … Gm(x2 + Δx, y2 + Δy) = 0 (2-m) (Δx, Δy) can be solved for each pair of two equations from two sets, so we have l ×

m pairs of (Δx, Δy). Hence, we need one more affirmative point to decide the best solu-


21

tion. Let (x3, y3) be the third affirmative point and mapped to h consist of H1, H2, …, Hn. Then, the position fix application calculates Hi for each l × m pair of (Δx, Δy), creating l × m × n results. Namely,

Hi(x3 + Δx, y3 + Δy), for i (1 ≤ i ≤ n), (3)

are calculated. Finally, we can select Hi and corresponding (Δx, Δy) that minimizes the criteria. With this, the coordinates of an ambiguous point is corrected to recalculate map matching procedure.

Fig. 3. Link accuracy criteria.

3.3 Cooperative Position Fix Scheme

Each telematics device also has GPS receiver installed. Typically, the GPS receiver gives a record which consists of time, latitude, longitude, speed, heading, and so on. Each telematics device stores those records just for the predefined time interval, which should be set lest the history log should cause the device memory to overflow. Generally, map matching is executed just when necessary. When an application invokes the map matching procedure, it first runs an embedded map matching algorithm with the specified GPS reading. In case it is difficult to decide to which link the coordinate belongs, namely, there are more than one candidate link, the device will trigger the cooperative position fix procedure.

The cooperative procedure is initiated by sending to its neighbors a query message with corresponding GPS time stamped. The 1-hop neighbors retrieve the record having the specified time index from their own history log, run the map matching locally, and reply the result. The result also contains the assurance level that the result is correct, that is, whether it is an affirmative point. The issuer device collects the replies. If the number of available affirmative points is less than 3, it will reissue the query to its 2-hop neigh-bors through 1-hop neighbors and so on until it collects sufficient number of affirmative points or fails to meet the time constraints given by the application. 3.4 Miscellanies

To decide weather a point is affirmative point or not, the telematics device runs a map matching procedure to find both the best and second best candidates. The ratio of perpendicular distances to each of them can be calculated, and the larger this ratio, the more affirmative is the point. We empirically classify a point as the affirmative point


22

when the ratio is larger than or equal to 3.0. This value seems relatively large, but the value less that 3.0 can lead to a wrong selection of affirmative points, especially when they are near intersections, where small difference may result in the sharp increase in the distance ratio. Actually, among tens of thousands of points collected in one day, almost 32% of GPS records are classified to be ambiguous points, including the one that cannot find any candidate due to occasional unrecoverable GPS error. When the number of af-firmative points collected from its neighbors exceeds 3, they are sorted by above-men- tioned distance ratio, then, f, g, and h are selected. In addition, our scheme needs |l × m × n| calculations of linear equations, so the computation time depends on the number of total segments that constitute the candidate link. If an affirmative point is bound to a link having too many segments, it can be dropped from the affirmative point list to reduce the computation time.

Up to now, we assumed that digital map has no error. However, the error is also in-evitable, possibly being different even in a same segment, to say nothing of between dif-ferent segments. The incorrect segment is especially harmful when it is bound to an af-firmative point. In that case, the inaccuracy of the segment propagates to the correction term, namely, (Δx, Δy), resulting in additional error to the ambiguous point and accord-ingly much worse match. Hence, it is desirable to consider the accuracy of the link when we pick the affirmative point. To this end, each telematics device needs to maintain the accuracy of each link. If a link has just one segment, it has a high possibility that it is accurate, as the link is straight. When performing the procedure described in section 3.2, sometimes (Δx, Δy) are very small values almost equal to 0. In that case, all the three links, namely, f, g, and h can be judged to be accurate when they are single-segment links. In the subsequent step, the selection procedure can give precedence to a point bound to such a link in selecting affirmative points.

4. PERFORMANCE MEASUREMENT

Taxi telematics system described in section 2.1 creates a great deal of GPS records every day, reaching tens of thousands. These records make it possible for us to access the proposed position fix scheme on the assumption that records of a common time stamp is collected by a group management protocol. To build the data set necessary to evaluate the proposed algorithm, records are grouped by the time index and those groups are ex-tracted whose members are located within 20 km and the number of them is larger than 3. For each group, the experiment applied the method proposed in section 3. Actually, it is impossible to investigate the actual path taken by each vehicle for such a large amount of data, so the correctness of map matching result cannot be measured. Hence, we chose assurance level as the performance criteria. The assurance level can be quantified by the ratio of perpendicular distances from a point to the best and the second best matching links.

Fig. 4 exhibits the execution result of the program code implemented according to the proposed scheme. In this figure, × represents the raw GPS data while ○ the corrected coordinate. When they overlap, it means that our scheme cannot improve position accu-racy. In other cases, this figure indicates that the corrected positions are closer to the ac-tual road than the original GPS readings.


23

Fig. 4. Execution results.

Fig. 5. Sharpness distribution.

Fig. 5 plots the assurance level distribution for collected (non-cooked) GPS data as

well as corrected GPS data according to the proposed scheme. The assurance level dis-tributes form 1.0 to infinity. In this figure, the assurance level greater than 3.0 is also considered to be 3.0, as the second match is meaningless.

When all members are affirmative points or the number of affirmative points is less than 3, we cannot expect the accuracy improvement. After all, Fig. 5 indicates that the proposed scheme can reduce ambiguous points from 32.0% to 20.6%. The ratio of af-firmative points was also increased from 61.0% to 71.2%, sharpening the gap between the best two matches. About 5% of corrected points have worse affinity or sometimes could not find a candidate after the coordinate adjustment. This is due to the wrong se-lection of affirmative points in the group. Sometimes, the points seem to have experienced different GPS error deviation, and in that case, our scheme fails in getting a rea-sonable position fix.


24

5. CONCLUSIONS AND FUTURE WORK

This paper has proposed and evaluated the performance of a cooperative position fix scheme, aiming at enhancing the accuracy of map matching and thus the quality of di-verse location-based services, without extra equipment. Designed on the telematics net-work consisting of many in-vehicle telematics devices moving on the road network, each device can collect the GPS readings from its neighbors via the legacy membership man-agement mechanism supported typically in the embedded operating system. In map matching, with the sufficient number of affirmative points, the ambiguity of a point can be reduced. For the group of GPS records sharing the same time index, the affirmative point makes it possible for telematics devices to cooperatively calculate the position fix. The performance measurement result, conducted with the actual GPS data obtained from a location tracking system currently in operation, shows that the proposed scheme can reduce ambiguous points from 32.0% to 20.6%. The ratio of points having assurance level larger than 3.0 was also increased from 61.0% to 71.2%, sharpening the gap be-tween the best two matches. The improved accuracy can provide a useful and efficient framework for advanced services to be designed, developed, and tested, especially on the vehicular network.

For future work, we are first planning to test the proposed scheme on actual VANET. In addition, various factors will be considered together in selecting an affirma-tive point including available satellites, link accuracy, and so on. Then, the algorithm will be extended to the off-line analysis such as route tracking, with the combination of some artificial intelligence techniques [15].

REFERENCES

1. http://en.wikipedia.org/wiki/Telematics. 2. S. Brakatsoulas, D. Pfoser, R. Salas, and C. Wenk, “On map-matching vehicle track-

ing data,” in Proceedings of the 31st Very Large Database Conference, 2005, pp. 853-864.

3. http://www.microsoft.com/windowsautomotive/default.mspx. 4. M. Hazas, J. Scott, and J. Krumm, “Location-aware computing comes of age,” IEEE

Computer, 2004, pp. 95-97. 5. W. Y. Ochieng, M. Quddus, and R. B. Noland, “Map-matching in complex urban

road networks,” Brazilian Journal of Cartography, Vol. 55, 2003, pp. 1-18. 6. J. Yang, S. Kang, and K. Chon, “The map matching algorithm of GPS data with rela-

tively long polling time intervals,” Journal of Eastern Asia Society for Transporta-tion Studies, Vol. 6, 2005, pp. 2561-2573.

7. M. Imamura, K. Kobayashi, and K. Watanabe, “Real-time positioning by fusing dif-ferential-GPS and local vehicle sensors,” in Proceedings of Society of Instrument and Control Engineers Annual Conference, 2003, pp. 778-781.

8. W. Sun, H. Yamaguchi, K. Yukimasa, and S. Kusumoto, “GVGrid: A QoS routing protocol for vehicular ad hoc networks,” in Proceedings of the 14th IEEE Interna-tional Workshop on Quality of Service, 2006, pp. 130-139.

9. O. Wolfson, B. Xu, H. Yin, and N. Rishe, “Resource discovery using spatio-tempo-


25

ral information in mobile ad-hoc networks,” in Proceedings of International Work-shop on Web and Wireless Geographical Information Systems, 2006, pp. 129-142.

10. J. Krumm, J. Letchner, and E. Horvitz, “Map matching with travel time constraints,” in Proceedings of the Society of Automotive Engineers World Congress, 2007.

11. J. Lee, G. Park, H. Kim, Y. Yang, P. Kim, and S. Kim, “A telematics service system based on he Linux cluster,” Lecture Note in Computer Science, Vol. 3340, 2007, pp. 660-667.

12. H. Wu, R. Fujimoto, R. Guensler, and M. Hunter, “MDDV: A mobility-centric data dissemination algorithm for vehicular networks,” in Proceedings of ACM Interna-tional Workshop on Vehicular Ad-hoc Networks, 2004, pp. 47-56.

13. J. Krumm, “The geographic context browser,” in Proceedings of International Work- shop on Exploiting Context Histories in Smart Environments, 2005, http://www.per- vasive.ifi.lmu.de/workshops/w8/papers/echise2005-s05-GeographicContextBrowser- Krumm.pdf.

14. C. Chow, H. Leong, and A. Chan, “Distributed group-based cooperative caching in a mobile broadcast environment,” in Proceedings of International Conference on Mo-bile Data Management, 2005, pp. 97-106.

15. C. Chow, H. Leong, and A. Chan, “Group-based cooperative cache management for mobile clients in a mobile environment,” in Proceedings of International Confer-ence on Parallel Processing, 2004, pp. 83-90.

16. M. Sun, W. Feng, T. Lai, K. Yamada, and H. Okada, “GPS-based message broadcast for adaptive inter-vehicle communications,” in Proceedings of Vehicular Technology Conference, 2000, pp. 2685-2692.

17. J. H. Lee, G. L. Park, and M. J. Kang, “A message scheduling scheme in hybrid telematics networks,” Lecture Note on Computer Science, Vol. 5072, 2008, pp. 769- 779.

18. M. Grossniklaus, M. Norrie, B. Singer, and N. Weibel, “Putting location-based ser-vices on the map,” in Proceedings of the 6th International Symposium on Web and Wireless Geographical Information System, 2006, pp. 1-11.

JungHoon Lee received the B.S., M.S., and Ph.D. from Department of Computer Engineering, Seoul National University, Korea. From 1990 to 1992, and also in 1996, he was a senior re-search engineer at Lab. of Optical Telecommunication, Daewoo Telecom, Republic of Korea. In 1997, he joined the Department of computer Science and Statistics at Cheju National University. From 2003 to 2005, he was a visiting scholar at Department of Computer Science, University of Texas at Austin. His research interests include real-time communication and wireless network.


26

Gyung-Leen Park received B.S. in Department of Com-puter Science from Chung-Ang University. He received M.S. and Ph.D. from Computer Science and Engineering department at the University of Texas at Arlington, respectively. His research in-terests include scheduling in parallel and distributed systems, mobile computing, and telematics. In 1997, he was an assistant professor at the University of Texas at Arlington. In 1998, he joined the Department of Computer Science and Statistics at Cheju National University, Korea. Currently, he is the director of the ITRC of Cheju National University, Korea.

Chang Oan Sung completed his Ph.D. in Computer Science from University of Wyoming. He is currently an assistant Pro-fessor in the Department of Computer Science at Indiana Univer-sity Southeast. Dr. Sung is interested in software engineering, security, and database. He studies are about process models and their uses in software engineering. Dr. Sung also has investigated data mining and security under networks such as P2P. He has published tens of refereed papers in journals and conference pro-ceedings. He has developed and consulted applications for com-panies including armed forces since 1990. Dr. Sung has served as a co-chair of ACM SAC software engineering track since 2006, and a special issue guest editor for Software Quality Journal.

Hyung Do Choi received the M.S. and Ph.D. degree in Ma-terial Sciences from Korea University in 1989 and 1996, respec-tively. Since 1997, he has been with Electronics and Telecommu-nications Research Institute, Korea, where he is presently a prin-cipal member of Radio Technology Research Department. He has carried out research in field of biological effects of RF radiation and developed RF radiation protection standards. His current re-search interests include EMC analysis SW, EMC countermeasure, electromagnetic wave absorber design, and absorbing and shield-ing materials.

a cooperative position fix scheme based on a group management on

Documents